The purpose of item encoding layer is to capture the high-level information of item \(v\)  in knowledge graph and generate a better item's representation vector.
Here, item \(v\)  captures high-level neighbor information through v's l-level neighbors (\(\mathcal {E}_v^l\) ) and v's l-level triples (\(S_v^l\) ). As \(l\)  increases, the elements in \(S_v^l\)  increase exponentially. In order to reduce the calculation time, we collect \(k\)  neighbors of each node. This method is a commonly used simple but effective strategy to reduce computational cost [1]}, [2]}.
\(\mathcal {E}_v^l=\lbrace t|t\in \mathcal {G} \quad and \quad (h,r,t)\in {S_v^l}\rbrace \quad l=1,2,3,...\) 
\(S_v^l=\lbrace (h,r,t)|h\in \mathcal {E}_v^{l-1} \quad and \quad (h,r,t)\in \mathcal {G}\quad and \quad t\notin \mathcal {E}_{v}^j\rbrace \quad l=1,2,3,...\quad j<l\) 
\(\mathcal {E}_v^0=\lbrace v\rbrace \) 
